Patients take care of what their teeth will be like after the orthodontics. Orthodontists usually describe the expectation movement based on the original smile images, which is unconvincing. The growth of deep-learning generative models change this situation. It can visualize the outcome of orthodontic treatment and help patients foresee their future teeth and facial appearance. While previous studies mainly focus on 2D or 3D virtual treatment outcome (VTO) at a profile level, the problem of simulating treatment outcome at a frontal facial image is poorly explored. In this paper, we build an efficient and accurate system for simulating virtual teeth alignment effects in a frontal facial image. Our system takes a frontal face image of a patient with visible malpositioned teeth and the patient's 3D scanned teeth model as input, and progressively generates the visual results of the patient's teeth given the specific orthodontics planning steps from the doctor (i.e., the specification of translations and rotations of individual tooth). We design a multi-modal encoder-decoder based generative model to synthesize identity-preserving frontal facial images with aligned teeth. In addition, the original image color information is used to optimize the orthodontic outcomes, making the results more natural. We conduct extensive qualitative and clinical experiments and also a pilot study to validate our method.

Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.

The long-standing theory that a colour-naming system evolves under the dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies including the analysis of four decades' diachronic data from the Nafaanra language. This inspires us to explore whether artificial intelligence could evolve and discover a similar colour-naming system via optimising the communication efficiency represented by high-level recognition performance. Here, we propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images. Given an RGB image, Annotation Branch maps it into an index map before generating the quantised image with a colour palette, meanwhile the Palette Branch utilises a key-point detection way to find proper colours in palette among whole colour space. By interacting with colour annotation, CQFormer is able to balance both the machine vision accuracy and colour perceptual structure such as distinct and stable colour distribution for discovered colour system. Very interestingly, we even observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining a high performance in high-level recognition tasks such as classification and detection. Extensive experiments demonstrate the superior performance of our method with extremely low bit-rate colours. We will release the source code soon.

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.

融合激光雷达和相机信息对于在自动驾驶系统中实现准确可靠的3D对象检测至关重要。但是，由于难以结合两个截然不同的方式的多晶格几何和语义特征，因此这是具有挑战性的。最近的方法旨在通过2D摄像机图像中的提升点（称为种子）中的3D空间来探索相机功能的语义密度，并且可以将它们大致分为1）1）原始点的早期融合，旨在增强3D在早期输入阶段的点云，以及2）Bev（鸟眼视图）的后期融合，在检测头之前合并了LiDar和Camera BEV功能。尽管两者在增强联合特征的表示能力方面都具有优点，但这种单级融合策略是对上述挑战的次优点。他们的主要缺点是无法充分从两种不同的方式中相互作用的多晶格语义特征。为此，我们提出了一个新颖的框架，该框架着重于多粒性激光雷达和相机功能的多尺度渐进互动。我们提出的方法缩写为MDMSFusion，实现最先进的方法可导致3D对象检测，在Nuscenes验证集上具有69.1 MAP和71.8 NDS，在NUSCENES测试集上进行了70.8 MAP和73.2 nds，该级别的第一和第二级和第二个NDS。在提交时，在单模型的非集结方法中。

对比度学习（CL）最近已应用于对抗性学习任务。这种实践将对抗样本视为实例的其他积极观点，并且通过彼此达成最大的协议，可以产生更好的对抗性鲁棒性。但是，由于对抗性扰动可能会导致实例级别的身份混乱，因此这种机制可能存在缺陷，这可能会通过用单独的身份将不同的实例聚集在一起来阻碍CL性能。为了解决这个问题，我们建议在形成鲜明对比时不平等地对待对抗样本，与不对称的Infonce目标（$ a-Infonce $）允许区分对抗样本的考虑。具体而言，对手被视为降低的阳性，会引起较弱的学习信号，或者是与其他负面样本形成较高对比的艰难负面因素。以不对称的方式，可以有效地减轻CL和对抗性学习之间相互冲突目标的不利影响。实验表明，我们的方法始终超过不同鉴定方案的现有对抗性CL方法，而无需额外的计算成本。提出的A-INFONCE也是一种通用形式，可以很容易地扩展到其他CL方法。代码可从https://github.com/yqy2001/a-infonce获得。

评估对象图像的模糊对于提高对象识别和检索的性能至关重要。主要挑战在于缺乏具有可靠标签和有效学习策略的丰富图像。当前的数据集标记为有限且混乱的质量水平。为了克服这一限制，我们建议将成对图像之间的等级关系标记，而不是它们的质量水平，因为人类更容易标记，并建立具有可靠标签的大规模逼真的面部图像模糊评估数据集。基于此数据集，我们提出了一种仅以成对等级标签作为监督的方法来获得模糊分数。此外，为了进一步提高绩效，我们提出了一种基于四倍体排名一致性的自制方法，以更有效地利用未标记的数据。受监督和自我监督的方法构成了最终的半监督学习框架，可以端对端训练。实验结果证明了我们方法的有效性。

3D密集字幕是最近提供的新型任务，其中点云包含比2D对应物更多的几何信息。但是，由于点云中包含的更高复杂性和更广泛的对象关系，它也更具挑战性。现有方法仅将这种关系视为图表中对象特征学习的副产品，而无需特别编码它们，从而导致了亚最佳结果。在本文中，旨在通过捕获和利用3D场景中的复杂关系来改善3D密集的字幕，我们提出了更多的多阶关系挖掘模型，以支持产生更多的描述性和全面标题。从技术上讲，我们更多地以渐进的方式编码对象关系，因为可以从有限数量的基本关系中推论复杂的关系。我们首先设计了一种新型的空间布局图卷积（SLGC），该图形将几个一阶关系编码为在3D对象建议上构造的图的边缘。接下来，从结果图中，我们进一步提取多个三重态，这些三重态将基本的一阶关系封装为基本单元，并构造几个以对象为中心的三重态注意图（OTAG），以推断每个目标对象的多阶关系。将OTAG的更新的节点功能聚合并输入标题解码器，以提供丰富的关系提示，因此可以生成包括与上下文对象的不同关系的字幕。 SCAN2CAP数据集的广泛实验证明了我们提出的更多及其组件的有效性，并且我们也表现优于当前最新方法。我们的代码可从https://github.com/sxjyjay/more获得。

近年来，抑郁症的发病率在全世界迅速上升，但大规模的抑郁症筛查仍然具有挑战性。步态分析提供了抑郁症的非接触，低成本和高效的早期筛查方法。然而，基于步态分析的抑郁症的早期筛查缺乏足够的有效样本数据。在本文中，我们提出了一种用于评估抑郁症风险的骨架数据增强方法。首先，我们提出了五种技术来增加骨架数据并将其应用于抑郁和情感数据集。然后，我们将增强方法分为两种类型（非噪声增强和噪声增强），基于互信息和分类准确性。最后，我们探索了哪些增强策略可以更有效地捕捉人骨架数据的特征。实验结果表明，保留了更多原始骨架数据属性的增强训练数据集确定了检测模型的性能。具体而言，旋转增强和通道掩码增强使抑郁检测精度分别达到92.15％和91.34％。